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A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

Authors
  • Sornborger, Andrew T1
  • Lauderdale, James D2
  • 1 Department of Mathematics, University of California, Davis, CA.
  • 2 Department of Cellular Biology, University of Georgia, Athens, GA. , (Georgia)
Type
Published Article
Journal
Conference record. Asilomar Conference on Signals, Systems & Computers
Publication Date
Nov 01, 2016
Volume
2016
Pages
1056–1060
Identifiers
DOI: 10.1109/ACSSC.2016.7869531
PMID: 28649174
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C(τ), as opposed to standard methods that decompose the time series, X(t), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

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